Multichannel parallelizable sliding window RLS and fast RLS algorithms with linear constraints

摘要

This paper presents new sliding window (SW) recursive least squares (RLS) and fast RLS algorithms for adaptive filtering with linear constraints. The algorithms are formulated for the general case of multichannel adaptive filters with complex-valued weights, and are based on Papaodysseus's matrix inversion lemma. The lemma is used for SW correlation matrix inversion and for the inversion of some other matrices that appear in constrained SW RLS algorithms. The algorithms have a form that can be implemented by means of at least two parallel processors. The proposed algorithms can be used for time-domain adaptive filtering of non-stationary signals, whilst restricting the frequency response of the filter to specific values at particular frequencies. In addition, the algorithms can be used in linearly constrained adaptive beamforming, dealing with non-stationary interference. (c) 2005 Elsevier B.V. All rights reserved.